Wireless signal characterization is a growing area of research and an essential tool to enable spectrum monitoring, tactical signal recognition, spectrum management, signal authentication for secure communication, and so on. Recent years have witnessed several deep neural network models to perform single task signal characterization such as radio fingerprinting for emitter identification, automatic modulation classification, spectrum sharing, etc. However, with the emergence of 5G and the prospects of beyond 5G communication, there has been an increased deployment of edge devices that requires lightweight neural network models to perform signal characterization. To this end, a multi-task learning model that can perform multiple signal characterization tasks with a single neural network model has been proposed. However, due to the novel nature of multi-task learning as applied to signal characterization, there is a lack of a corresponding dataset with multiple labels for each waveform. In this paper, we openly share a synthetic wireless waveforms dataset suited for modulation recognition and wireless signal (protocol) classification tasks separately as well as jointly. The waveforms comprise radar and communication waveforms generated with GNU Radio to represent a heterogeneous wireless environment.

无线信号表征是一个不断发展的研究领域，也是实现频谱监测、战术信号识别、频谱管理、安全通信信号认证等的重要工具。近年来见证了几种深度神经网络模型来执行单任务信号表征，例如用于发射器识别的无线电指纹识别、自动调制分类、频谱共享等。 然而，随着 5G 的出现和超越 5G 通信的前景，出现了边缘设备的部署增加，需要轻量级神经网络模型来执行信号表征。为此，提出了一种多任务学习模型，可以用单个神经网络模型执行多个信号表征任务。然而，由于应用于信号表征的多任务学习的新颖性，缺乏每个波形具有多个标签的相应数据集。在本文中，我们公开共享了一个合成无线波形数据集，该数据集适用于单独和联合的调制识别和无线信号（协议）分类任务。波形包括使用 GNU Radio 生成的雷达和通信波形，以表示异构无线环境。